Research On 3D Trajectory Dynamic Planning Of Electric Patrol UAV

As an air information collection platform,UAV has high efficiency and safety performance in the development and utilization of power inspection technology.In the power inspection,whether the UAV has the ability of independent risk avoidance is related to the safety of UAV in the process of inspection information collection.At present,as the core part of UAV in two-dimensional plane,UAV is easy to achieve the effect of avoiding danger,but when it is applied in three-dimensional space,the effect of avoiding danger is general.This paper studies the dynamic path planning of UAV in three-dimensional space.This paper mainly discusses in several aspects.Firstly,it collects and analyzes some problems that UAV may encounter in three-dimensional space at home and abroad.At the same time,it models the three-dimensional space of UAV.Secondly,by comparing the intelligent path optimization algorithm of UAV,it is concluded that ant colony algorithm is the best algorithm.Based on the ant colony system,an improved ant colony algorithm with angle factor is proposed.By adjusting the local search strategy of the algorithm,the convergence efficiency of the algorithm is improved and the flight path of UAV is shortened.MATLAB simulation shows that the shortest path length with angle factor is 120 km,while the shortest distance of traditional ant colony algorithm is 142 km,and the overall distance is shortened by22 km.The improved algorithm has faster convergence times and faster convergence efficiency.Finally,in the three-dimensional environment with dynamic obstacles,the improved ant colony algorithm can not meet the requirements of UAV autonomous avoidance of dynamic obstacles,so the improved ant colony algorithm is combined with particle swarm algorithm to achieve the effect of UAV autonomous avoidance.Using the complementary advantages of the convergence speed of particle swarm optimization and ant colony algorithm,the fusion design is implemented,and the adaptive particle swarm optimization algorithm is introduced.By dynamically adjusting the inertia weight and learning factor,the improved algorithm can not only achieve the autonomous avoidance effect of UAV,but also strengthen the convergence of the algorithm.After testing with the test function,the simulation results show that the fusion algorithm has better comprehensive performance than other single algorithms,and the UAV can successfully avoid both static and dynamic obstacles,which can be used in the research of UAV dynamic obstacle avoidance and has far-reaching influence.